Papers by Ravneet Arora
Identifying Named Entities as they are Typed (2021.eacl-main)
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| Challenge: | Named Entity Recognition (NER) systems are not applicable to systems that process text in real time as the text is typed. |
| Approach: | They propose a new experimental setup for evaluating Named Entity Recognition systems that evaluates named entities as they are typed on a sentence level . they propose to adapt existing evaluation setups to suit the new setup . |
| Outcome: | The proposed setup shows that the best systems that are evaluated on each token after its typed reach performance within 1–5 F1 points of systems that were evaluated at the end of the sentence. |
On the Use of Context for Predicting Citation Worthiness of Sentences in Scholarly Articles (2021.naacl-main)
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| Challenge: | citation worthiness is an emerging research topic in the natural language processing domain . citation recommendation systems are often approached as ranking problems . |
| Approach: | They propose a hierarchical biLSTM-based model that uses two adjacent sentences to solve a citation worthiness problem. |
| Outcome: | The proposed approach can be applied to a dataset of over two million sentences and their labels. |
Learning Rich Representation of Keyphrases from Text (2022.findings-naacl)
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| Challenge: | Prior work has referred to extractive (part of document) or abstractive (not part of document). |
| Approach: | They propose to use a new pre-training objective to introduce keyphrases into transformer language models in discriminative and generative settings. |
| Outcome: | The proposed model improves performance in discriminative and generative settings and also improves on named entity recognition, question answering, relation extraction and abstractive summarization tasks. |
A Semi-Markov Structured Support Vector Machine Model for High-Precision Named Entity Recognition (P19-1)
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| Challenge: | Named entity recognition (NER) is the backbone of many NLP solutions. |
| Approach: | They propose a neural semi-Markov structured support vector machine model that controls the precision-recall trade-off by assigning weights to different types of errors in the loss-augmented inference during training. |
| Outcome: | The proposed model achieves better precision-recall trade-off at various precision levels. |